| According to all known laws of aviation, there is no way a bee should be able to fly. | |
| Its wings are too small to get its fat little body off the ground. | |
| The bee, of course, flies anyway because bees don't care what humans think is impossible. | |
| Yellow, black. Yellow, black. Yellow, black. Yellow, black. | |
| Ooh, black and yellow! | |
| Let's shake it up a little. | |
| Barry! Breakfast is ready! | |
| Coming! | |
| Hang on a second. | |
| Hello? |
This document captures learnings about how Agent Skills handle references/, recipes/, and services/ folders, including token budget implications and best practices based on the AgentSkills.io specification and GitHub Copilot implementation behavior.
Agent Skills use a three-tier loading model to efficiently manage LLM context windows:
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| #!/bin/bash | |
| now=`date +"%Y-%m-%d"` | |
| directory=~/Documents//Overleaf/backup | |
| outputTmpDirectory=$directory/tmp.zip | |
| outputDirectory=$directory/$backup.zip | |
| # To get the cookie | |
| cookie=`python - << EOF | |
| import re | |
| # pip install mechanize |
| #!/bin/bash | |
| now=`date +"%Y-%m-%d"` | |
| directory=~/Documents//Overleaf/backup | |
| outputTmpDirectory=$directory/tmp.zip | |
| outputDirectory=$directory/$backup.zip | |
| # To get the cookie | |
| cookie=`python - << EOF | |
| import re | |
| # pip install mechanize |
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
